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We propose a novel method to analyze how human operators use hydraulic manipulators of heavy-duty equipment. The approach is novel in the sense that it applies knowledge of motion planning and optimization techniques used in robotics. As an example, we consider the case of operating a forestry crane. To that end, we use motion data that has been recorded during standard operation with the help of sensors and a data acquisition unit. The data backs up the notion that operators work by performing repeatable patterns observed in the trajectories of the manipulator's joints. We show how this nominal behavior is computed, and consequently this allow us to present the following: 1) an analytical procedure to analyze motions, 2) how to represent the "performance" of the operator in a 2D plot, 3) an example of how to use this information to suggest customized control settings, and 4) some complementary ideas needed for improving efficiency through automation.

Working with forestry machines requires great amount of training to be sufficiently skilledto operate forestry cranes. In view of this, introducing automated motions, as those seenin robotic arms, is ambitioned by this industry for shortening the amount of training timeand make the work of the operator easier. Motivated by this fact, we have developedtwo experimental platforms for testing control systems and motion planning algorithms inreal-time. They correspond to a laboratory setup and a commercial version of a hydraulicmanipulator used in forwarder machines. The aim of this article is to present the results ofthis development by providing an overview of our trajectory planning algorithm and motioncontrol method, with a subsequent view of the experimental results. For motion control,we design feedback controllers that are able to track reference trajectories based on sensormeasurements. Likewise, we provide arguments to design controllers in open-loop for thecase of machines lacking of sensing devices. Relying on the tracking efficiency of thesecontrollers, we design time efficient reference trajectories of motions that correspond tologging tasks. To demonstrate performance, we provide an overview of an extensive testingdone on these machines.

The process of forest harvesting is highly mechanized in most industrialized countries, with felling and processing of trees performed by technologically advanced forestry machines. However, the maneuvering of the vehicles through the forest as well as the control of the on-board hydraulic boom crane is currently performed through continuous manual operation. This complicates the introduction of further incremental productivity improvements to the machines, as the operator becomes a bottleneck in the process. A suggested solution strategy is to enhance the production capacity by increasing the level of automation. At the same time, the working environment for the operator can be improved by a reduced workload, provided that the human-machine interaction is adapted to the new automated functionality.

The objectives of this thesis are 1) to describe and analyze the current logging process and to locate areas of improvements that can be implemented in current machines, and 2) to investigate future methods and concepts that possibly require changes in work methods as well as in the machine design and technology. The thesis describes the development and integration of several algorithmic methods and the implementation of corresponding software solutions, adapted to the forestry machine context. Following data recording and analysis of the current work tasks of machine operators, trajectory planning and execution for a specific category of forwarder crane motions has been identified as an important first step for short term automation. Using the method of path-constrained trajectory planning, automated crane motions were demonstrated to potentially provide a substantial improvement from motions performed by experienced human operators. An extension of this method was developed to automate some selected motions even for existing sensorless machines. Evaluation suggests that this method is feasible for a reasonable deviation of initial conditions.

Another important aspect of partial automation is the human-machine interaction. For this specific application a simple and intuitive interaction method for accessing automated crane motions was suggested, based on head tracking of the operator. A preliminary interaction model derived from user experiments yielded promising results for forming the basis of a target selection method, particularly when combined with some traded control strategy. Further, a modular software platform was implemented, integrating several important components into a framework for designing and testing future interaction concepts. Specifically, this system was used to investigate concepts of teleoperation and virtual environment feedback. Results from user tests show that visual information provided by a virtual environment can be advantageous compared to traditional video feedback with regards to both objective and subjective evaluation criteria.

Virtual environment-assisted teleoperation has great potential as a human-robot interaction paradigm for field robotic systems, in particular when combined with elements of automation. Unstructured outdoor environments present a complex problem with many challenging elements. For the specific application of forestry machines, we investigate which steps are required in order to implement such a system, what potential benefits there are, and how individual components can be adapted to efficiently assist forestry machine operators in their daily work in the near future. An experimental prototype of a teleoperation system with virtual environment-based feedback is constructed using a scenario-based design process. The feasibility of the implementation is partly verified through experimental studies.

Successful recognition and pose estimation of logs and trees as well as workspace modeling in the forest environment is essential for extensive automation of the harvesting and logging tasks of forestry machines. However, the free-form features of logs, few reliable textural features, large edge extraction errors, and segmentation faults caused by the barks on the surface of the logs present clear challenges for recognition and classification. To solve these problems, robust algorithms able to recognize and estimate poses of a variety of objects even under poor and partial inputs need to be developed.In this paper we focus on the most relevant task of recognizingand estimating postures of a bunch of logs located on the groundwith varying orientation and distance. Experiments carried outwith the help of a structured light camera demonstrate the feasibility of the proposed algorithm.

A short term goal in the forest industry is semi-automation of existing machines for the tasks of logging and harvesting. One way to assist drivers is to provide a set of predefined trajectories that can be used repeatedly in the process. In recent years much effort has been directed to the design of control strategies and task planning as part of this solution. However, commercialization of such automatic schemes requires the installation of various sensing devices, computers and most of all a redesign of the machine itself, which is currently undesired by manufacturers. Here we present an approach of implementing predefined trajectories in an open-loop fashion, which avoids the complexity of sensor and computer integration. The experimental results are carried out on a commercial hydraulic crane to demonstrate that this solution is feasible in practice.

We consider trajectory planning for an underactuated 3DOF helicopter, using the virtual holonomic constraint approach. First we choose constraint functions that describe the configuration variables along a desired motion in terms of some independent parametrization variable. This lets us describe the closed-loop system by some reduced order dynamics, the solution of which gives a feasible trajectory for the desired motion. By using the method of transverse linearization for controller design, we achieve exponential orbital stability to a desired trajectory. Numerical simulations confirm this property and show good convergence to a desired periodic motion when initialized from a resting state.

In the forest industry, trees are logged and harvested by human-operated hydraulic manipulators. Eventually, these tasks are expected to be automated with optimal performance. However, with todays technology the main problem is implementation. While prototypes may have rich sensing information, real cranes lack certain sensing devices, such as encoders for position sensing. Automating these machines requires unconventional solutions. In this paper, we consider the motion planning problem, which involves a redesign of optimal trajectories, so that open loop control strategies can be applied using feed-forward control signals whenever sensing information is not available.

We consider trajectory planning for kinematically redundant manipulators used on forestry machines. The analysis of recorded data from human operation reveals that the driver does not use the full potential of the machine due to the complexity of the manipulation task. We suggest an optimization procedure that takes advantage of the kinematic redundancy so that time-efficient joint and velocity profiles along the path can be obtained. Differential constraints imposed by the manipulator dynamics are accounted for by employing a phase-plane technique for admissible path timings. Velocity constraints of the individual joints are particularly restrictive in hydraulic manipulators. Our study aims for semi-autonomous schemes that can provide assistance to the operator for executing global motions.

This paper presents a method for obstacle avoidance and path-finding amid circular objects. The input data are circles and the output is a sequence of circles. The output circles represent a possible path, to a target, for a holonomic mobile robot. The method uses a solution to the Apollonius Tangency problem to find the maximum spanning circles amid the input circles. The radii of the circles can be set by desired clearance to nearby obstacles, from sensor parameters, or model parameters from extracted features. The method is intuitive and rather easily implemented and suits well for mobile robots, especially mobile robots with circular shape. It can be implemented with a depth-first approach where the target bearing angle is used as criteria in a divide and conquer step. The method was tested on sensor data registered by a laser range finder.